• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.33-43
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• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.189-199
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• 2009

The amount of information on the Web is explosively growing as the Internet gains in popularity. However, only a small portion of the information on the Web is truly relevant or useful to the user. Thus, offering suitable information according to user demand is an important subject in information retrieval. In e-commerce, the recommender system is essential to revitalize commercial transactions, raise user satisfaction and loyalty towards the information provider. The existing recommender systems are mostly based on user data collected at servers, so user data are dispersed over several servers. Therefore, web servers that lack sufficient user behavior data cannot easily infer user preferences. Also, if the user visits the server infrequently, it may be hard to reflect the dynamically changing user's interest. This paper proposes a novel personalization system analyzing the user preference based on web documents that are accessed by the user on a user device. The system also identifies non-content blocks appearing repeatedly in the dynamically generated web documents, and adds weight to the keywords extracted from the hyperlink sentence selected by the user. Therefore, the system establishes at an early stage recommendation strategies for the web server that has little user data. Also, user profiles are generated rapidly and more accurately by identifying the information blocks. In order to evaluate the proposed system, this study collected web data and purchase history from users who have current purchase activity. Then, we computed the similarity between purchase data and the user profile. We confirm the accuracy of the generated user profile since the web page containing the purchased item has higher correlation than other item pages.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• The Journal of Korean Institute of Information Technology
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• v.17 no.4
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• pp.87-97
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• 2019

SRS was proposed for immediate processing of the meaning of sensor data on mobile devices independent from specific sensor networks and sensor type. However, each time new sensor data is received, sensor data inspection operations are performed repeatedly, and it cause resulting in low performance. App profile-based SRS has been proposed to resolve the problem. The app profile-based SRS has improved the SRS problem through the profile, but has been tested in a virtual simulation environment. After that the test was experimented in a real-time environment, but has not been tested with a variety of dynamic factors. Therefore, this paper experiment considering such as user mobility and sensor density in real-time environment. And this paper also evaluate performance of the App profile-based through analysis of the results of the experiment. As a result, app profile-based SRS is high influence by density and sensor type, and the number of sensor node is not influence.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.857-866
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• 2003

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques in order to analyze an audit data set. However. since they mainly analyze the average behavior of a user's activities, some anomalies can be detected inaccurately. In this paper, a new clustering algorithm for modeling the normal pattern of a user's activities is proposed. Since clustering can identify an arbitrary number of dense ranges in an analysis domain, it can eliminate the inaccuracy caused by statistical analysis. Also, clustering can be used to model common knowledge occurring frequently in a set of transactions. Consequently, the common activities of a user can be found more accurately. The common knowledge is represented by the occurrence frequency of similar data objects by the unit of a transaction as veil as the common repetitive ratio of similar data objects in each transaction. Furthermore, the proposed method also addresses how to maintain identified common knowledge as a concise profile. As a result, the profile can be used to detect any anomalous behavior In an online transaction.

• International Journal of Advanced Culture Technology
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• v.1 no.2
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• pp.13-17
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• 2013

Today, most of the companies have numerous issues to take advantage of the data within the organization. Modeling techniques could be described using profile and historical log data as a tool of data mining techniques. It is covered increasingly with data entry, research, processing, modeling and reporting components of the icon in the form of easy-to-use in many datamining tools. Visual data mining process can create a data stream. In this paper, customer behavior is predicted in pages or products, using the history profile analysis and the navigation items are necessary to predict unknown features.

• Journal of the Korea Society for Simulation
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• v.7 no.2
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• pp.153-165
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• 1998

In the Is-95 soft handoff scheme of CDMA, there occurs a tradeoff between improving quality due to the space diversity and additional resource management like updates of the active set during soft handoffs. In addition, if a mobile terminal is alternating two adjacent cells, a well-known phenomenon called ping-ping causes the resource management to be seriously degraded. By noting that in general the probability that a user initiates additional handoffs is exponentially decreased as the user has already handed over one or more times, we present a soft handoff algorithm making use of handoff profiles of mobile terminals to improve resource utilization. In the proposed algorithm the number of handoffs made so far during the call is recorded in the mobile profile and the profile data is used for adjusting handoff parameters such as the value of add or drop threshold (T_ADD or T_DROP). Through simulations, the result of the proposed algorithm is shown to improve the handoff performance by lowering the number of handoffs while simultaneously reducing resource waste.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.931-934
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• 2005

This paper describes an adaptive recommendation system that provides real-time personalized trading advice to the investors based on their profiles and trading information environment. A proposed system integrates Stochastic technical analysis and artificial neural network that incorporates an adaptive user modeling. The user model is constructed and updated based on initial user profile and recorded user interactions with the system. The information presented to each individual user is also tailor-made to fit the user's behavior and preference. A system prototype was implemented in JAVA. Experiments used to evaluate the system's performance were done on both human subjects and synthetic users. The results show our proposed system is able to rapidly learn to provide appropriate advice to different types of users.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.269-282
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• 2007

We propose a personalized concept-based retrieval technique that uses domain ontology. Proposed system consist or representative concept extraction, user profile construction, and concept-based retrieval stages. First, we extract representative concept with using technique form contents and create the domain ontology. We compose user profile analysis that uses domain ontology for personalized concept-based retrieval. To verify the efficiency of the proposed technique, we perform experiment for Internet site in the engineering area. The results of experiment show that the proposed technique using the domain ontology and user profiles is more efficient than the existing techniques. Hence, the proposed concept-based retrieval technique can be expected to contribute to the development of an efficient personalized recommendation system or e-Commerce system.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.2
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• pp.162-171
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• 2013

A general purpose aircraft mission performance analysis program has been developed. The program can be used in design mode or in analysis mode. Fuel weight for a given mission profile can be estimated when the design mode is chosen, while mission time or mission range for a given fuel can be estimated when the analysis mode is chosen. The mission analysis program is written with JAVA and includes GUI(Graphic User Interface) for users' conveniences. With a proper combination of databases for propulsion, aerodynamics and weight, the program can be configured to compute the performance of any type of aircraft. The program is validated by comparing its results with the results of a well known performance analysis program by ADD(Agency for Defense Development).